Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls

  • Authors:

  • S. P. Anbuudayasankar;K. Ganesh;S. C. Lenny Koh;Yves Ducq

  • Affiliations:

  • Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Ettimadai, Coimbatore 641105, Tamil Nadu, India;Global Business Services - Global Delivery, IBM India Private Limited, Bandra East, Mumbai 400051, Maharashtra, India;Logistics and Supply Chain Management (LSCM) Research Centre, Management School, The University of Sheffield, 9 Mappin Street, Sheffield S1 4DT, UK;University of Bordeaux, IMS-LAPS-GRAI - UMR 5218 CNRS, 351 cours de la Libération, 33405 Talence cedex, France

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

The cost of distribution and logistics accounts for a sizable part of the total operating cost of a company. However, the cost associated with operating vehicles and crews for delivery purposes form an important component of total distribution costs. Small percentage saving in these expenses could result in a large amount of savings over a number of years. Increase in the number of automated teller machines (ATMs) in the bank industry enforced the researchers to concentrate much on the optimization of distribution logistics problem. The process of replenishing money in the ATMs is considered as a scope with bi-objectives such as minimizing total routing cost and minimizing the span of travel tour. Some of the pick-up routes of the problem are forced and it is termed as forced backhauls. This problem is termed as bi-objective vehicle routing problems with forced backhauls (BVFB). We developed three heuristics to solve BVFB. Two heuristics are modified savings heuristics and the third heuristic is based on adapted genetic algorithm (GA). Standard data sets of VRPB of real life cases for BVFB and randomly generated datasets for BVFB are solved using all the three heuristics. The results are compared and found that all the three heuristics are competitive in solving BVFB. GA yields better solution compared to the other two heuristics.